査読付き論文

[A-23] Jun Koseki, Chie Motono, Keisuke Yanagisawa, Genki Kudo, Ryunosuke Yoshino, Takatsugu Hirokawa, Kenichiro Imai, “CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological Data Analysis”, Journal of Chemical Information and Modeling 65, 5567-5575, 2025/5. DOI: 10.1021/acs.jcim.4c02111

[A-22] Chie Motono, Keisuke Yanagisawa, Jun Koseki, Kenichiro Imai, “CrypTothML: An Integrated Mixed-Solvent Molecular Dynamics Simulation and Machine Learning Approach for Cryptic Site Prediction”, International Journal of Molecular Sciences 26, 4710, 2025/5. DOI: 10.3390/ijms26104710

[A-21] Jianan Li, Keisuke Yanagisawa, Yutaka Akiyama, “CycPeptMP: enhancing membrane permeability prediction of cyclic peptides with multi-level molecular features and data augmentation”, Briefings in Bioinformatics 25, bbae417, 2024/8. DOI: 10.1093/bib/bbae417

[A-20] Keisuke Yanagisawa, Takuya Fujie, Kazuki Takabatake, Yutaka Akiyama, “QUBO Problem Formulation of Fragment-Based Protein–Ligand Flexible Docking”, Entropy 26, 397, 2024/4. DOI: 10.3390/e26050397

[A-19] Genki Kudo, Keisuke Yanagisawa, Ryunosuke Yoshino, Takatsugu Hirokawa, “AAp-MSMD: Amino Acid Preference Mapping on Protein–Protein Interaction Surfaces Using Mixed-Solvent Molecular Dynamics”, Journal of Chemical Information and Modeling 63, 7768-7777, 2023/12. DOI: 10.1021/acs.jcim.3c01677

[A-18] Jianan Li, Keisuke Yanagisawa, Masatake Sugita, Takuya Fujie, Masahito Ohue, Yutaka Akiyama, “CycPeptMPDB: A Comprehensive Database of Membrane Permeability of Cyclic Peptides”, Journal of Chemical Information and Modeling 63, 2240-2250, 2023/3. DOI: 10.1021/acs.jcim.2c01573

[A-17] Masatake Sugita, Takuya Fujie, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Lipid Composition Is Critical for Accurate Membrane Permeability Prediction of Cyclic Peptides by Molecular Dynamics Simulations”, Journal of Chemical Information and Modeling 62, 4549-4560, 2022/9. DOI: 10.1021/acs.jcim.2c00931

[A-16] Keisuke Yanagisawa, Rikuto Kubota, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama, “Effective Protein–Ligand Docking Strategy via Fragment Reuse and a Proof-of-Concept Implementation”, ACS Omega 7, 30265-30274, 2022/8. DOI: 10.1021/acsomega.2c03470

[A-15] Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “Inverse Mixed-Solvent Molecular Dynamics for Visualization of the Residue Interaction Profile of Molecular Probes”, International Journal of Molecular Sciences 23, 4749, 2022/4. DOI: 10.3390/ijms23094749

[A-14] Kazuki Takabatake, Keisuke Yanagisawa, Yutaka Akiyama, “Solving Generalized Polyomino Puzzles Using the Ising Model”, Entropy 24, 354, 2022/2. DOI: 10.3390/e24030354

[A-13] Jianan Li, Keisuke Yanagisawa, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama, “Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning”, Bioinformatics 38, 1110-1117, 2021/11. DOI: 10.1093/bioinformatics/btab726

[A-12] 渓甫 柳澤, “タンパク質立体構造情報を用いた薬剤バーチャルスクリーニング”, JSBi Bioinformatics Review 2, 76-86, 2021/10. DOI: 10.11234/jsbibr.2021.9

[A-11] Kazuki Takabatake, Kazuki Izawa, Motohiro Akikawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Improved Large-Scale Homology Search by Two-Step Seed Search Using Multiple Reduced Amino Acid Alphabets”, Genes 12, 1455, 2021/9. DOI: 10.3390/genes12091455

[A-10] Masatake Sugita, Satoshi Sugiyama, Takuya Fujie, Yasushi Yoshikawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Large-Scale Membrane Permeability Prediction of Cyclic Peptides Crossing a Lipid Bilayer Based on Enhanced Sampling Molecular Dynamics Simulations”, Journal of Chemical Information and Modeling 61, 3681-3695, 2021/7. DOI: 10.1021/acs.jcim.1c00380

[A-9] Keisuke Yanagisawa, Yoshitaka Moriwaki, Tohru Terada, Kentaro Shimizu, “EXPRORER: Rational Cosolvent Set Construction Method for Cosolvent Molecular Dynamics Using Large-Scale Computation”, Journal of Chemical Information and Modeling 61, 2744-2753, 2021/6. DOI: 10.1021/acs.jcim.1c00134

[A-8] Masahiro Mochizuki, Shogo D. Suzuki, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “QEX: target-specific druglikeness filter enhances ligand-based virtual screening”, Molecular Diversity 23, 11-18, 2019/2. DOI: 10.1007/s11030-018-9842-3

[A-7] Takashi Tajimi, Naoki Wakui, Keisuke Yanagisawa, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama, “Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques”, BMC Bioinformatics 19, 527, 2018/12. DOI: 10.1186/s12859-018-2529-z

[A-6] Keisuke Yanagisawa, Shunta Komine, Rikuto Kubota, Masahito Ohue, Yutaka Akiyama, “Optimization of memory use of fragment extension-based protein–ligand docking with an original fast minimum cost flow algorithm”, Computational Biology and Chemistry 74, 399-406, 2018/6. DOI: 10.1016/j.compbiolchem.2018.03.013

[A-5] Takanori Hayashi, Yuri Matsuzaki, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions”, BMC Bioinformatics 19, 62, 2018/5. DOI: 10.1186/s12859-018-2073-x

[A-4] Shuntaro Chiba, Takashi Ishida, Kazuyoshi Ikeda, Masahiro Mochizuki, Reiji Teramoto, Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Chandrasekaran Ramakrishnan, A. Mary Thangakani, D. Velmurugan, M. Michael Gromiha, Tatsuya Okuno, Koya Kato, Shintaro Minami, George Chikenji, Shogo D. Suzuki, Keisuke Yanagisawa, Woong-Hee Shin, Daisuke Kihara, Kazuki Z. Yamamoto, Yoshitaka Moriwaki, Nobuaki Yasuo, Ryunosuke Yoshino, Sergey Zozulya, Petro Borysko, Roman Stavniichuk, Teruki Honma, Takatsugu Hirokawa, Yutaka Akiyama, Masakazu Sekijima, “An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes”, Scientific Reports 7, 12038, 2017/9. DOI: 10.1038/s41598-017-10275-4

[A-3] Keisuke Yanagisawa, Shunta Komine, Shogo D Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “Spresso: an ultrafast compound pre-screening method based on compound decomposition”, Bioinformatics 33, 3836-3843, 2017/3. DOI: 10.1093/bioinformatics/btx178

[A-2] Shuntaro Chiba, Kazuyoshi Ikeda, Takashi Ishida, M. Michael Gromiha, Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Kun-Yi Hsin, Hiroaki Kitano, Kazuki Yamamoto, Nobuyoshi Sugaya, Koya Kato, Tatsuya Okuno, George Chikenji, Masahiro Mochizuki, Nobuaki Yasuo, Ryunosuke Yoshino, Keisuke Yanagisawa, Tomohiro Ban, Reiji Teramoto, Chandrasekaran Ramakrishnan, A. Mary Thangakani, D. Velmurugan, Philip Prathipati, Junichi Ito, Yuko Tsuchiya, Kenji Mizuguchi, Teruki Honma, Takatsugu Hirokawa, Yutaka Akiyama, Masakazu Sekijima, “Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target”, Scientific Reports 5, 17209, 2015/11. DOI: 10.1038/srep17209

[A-1] Keisuke Yanagisawa, Takashi Ishida, Yutaka Akiyama, “Drug Clearance Pathway Prediction Based on Semi-supervised Learning”, IPSJ Transactions on Bioinformatics 8, 21-27, 2015/8. DOI: 10.2197/ipsjtbio.8.21

査読付き国際会議

[B-7] Kazuki Takabatake, Kazuki Izawa, Motohiro Akikawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Improved Homology Search for Metagenomic Analysis by Two-Step Seed Search with Reduced Amino Acid Alphabets”, The 10th International Conference on Bioinformatics and Biomedical Science (ICBBS2021), 2021/10/29–31.

[B-6] Kazuya Isawa, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Antisense oligonucleotide activity analysis based on opening and binding energies to targets”, In Proceedings of the 27th International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’21), 2021/7/27.

[B-5] Masahito Ohue, Ryota Ii, Keisuke Yanagisawa, Yutaka Akiyama, “Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph”, In Proceedings of the 25th International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’19), 2019/7/29.

[B-4] Takashi Tajimi, Naoki Wakui, Keisuke Yanagisawa, Yasushi Yoshikawa, Masahito Ohue, Yutaka Akiyama, “Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques”, The 29th International Conference on Genome Informatics (GIW 2018), 2018/12/4.

[B-3] Keisuke Yanagisawa, Shunta Komine, Rikuto Kubota, Masahito Ohue, Yutaka Akiyama, “Optimization of memory use of fragment extension-based protein-ligand docking with an original fast minimum cost flow algorithm”, The 16th Asia Pacific Bioinformatics Conference (APBC2018), 2018/1/15.

[B-2] Takanori Hayashi, Yuri Matsuzaki, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions”, The 16th Asia Pacific Bioinformatics Conference (APBC2018), 2018/1/15.

[B-1] Keisuke Yanagisawa, Shunta Komine, Shogo D. Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “ESPRESSO: An ultrafast compound pre-screening method based on compound decomposition”, The 27th International Conference on Genome Informatics (GIW 2016), 2016/10/4.

査読無し国際会議

[C-16] Chie Motono, Keisuke Yanagisawa, Jun Koseki, Kenichiro Imai, “CrypTothML: Cryptic Site Prediction using Mixed-Solvent Molecular Dynamics Simulation and Machine Learning”, The international Chemical Congress of Pacific Basin Societies (Pacifichem) 2025, 2025/12/15.

[C-15] Keisuke Yanagisawa, Takuya Fujie, Kazuki Takabatake, Yutaka Akiyama, “FraSCO-VS: Fragment-based drug virtual screening by combinatorial optimization with quantum annealer”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.

[C-14] Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “Quantitative Evaluation of Protein-Ligand Substructure Interaction with Inverse Mixed-Solvent Molecular Dynamics Simulation”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.

[C-13] Masatake Sugita, Yudai Noso, Jianan Li, Takuya Fujie, Keisuke Yanagisawa, Yutaka Akiyama, “Protocol for Membrane Permeability Prediction of Cyclic Peptides by Combining Molecular Dynamics Simulations and Machine Learning”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.

[C-12] Kaho Akaki, Keisuke Yanagisawa, Yutaka Akiyama, “Enhancing virtual screening accuracy by refining docking calculation scoring with mixed-solvent molecular dynamics”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.

[C-11] Masahiro Shimizu, Masatake Sugita, Keisuke Yanagisawa, Yutaka Akiyama, “An Automatic Iterative Refinement Protocol for Restraint Parameters in REUS Molecular Dynamics”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.

[C-10] Masayoshi Shimizu, Satoshi Yoneyama, Keisuke Yanagisawa, Yutaka Akiyama, “Development of a fast pre-screening method using compound retrieval by fragment pose pairs”, Asia Hub for e-Drug Discovery 2025 (AHeDD2025), 2025/9/24.

[C-9] Chie Motono, Jun Koseki, Keisuke Yanagisawa, Genki Kudo, Ryunosuke Yoshino, Takatsugu Hirokawa, Kenichiro Imai, “Cryptic site detection using machine learning based on mixed-solvent molecular dynamics simulations results”, Asia & Pacific Bioinformatics Joint Conference 2024, 2024/10/22.

[C-8] Jun Koseki, Chie Motono, Keisuke Yanagisawa, Genki Kudo, Ryunosuke Yoshino, Takatsugu Hirokawa, Kenichiro Imai, “Development of the Cryptic Site searching method with Mixed-solvent molecular dynamics and Topological data analyses methods”, Asia & Pacific Bioinformatics Joint Conference 2024, 2024/10/22.

[C-7] Keisuke Yanagisawa, Ryunosuke Yoshino, Genki Kudo, Takatsugu Hirokawa, “Quantitative Evaluation of Protein-Compound Substructure Interaction with Inverse Mixed-Solvent Molecular Dynamics Simulation”, 21st IUPAB and 62nd BSJ joint congress 2024 and 28P-189, 2024/6/28.

[C-6] Masatake Sugita, Takuya Fujie, Yudai Noso, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Development and Application of a Protocol for Predicting Membrane Permeability of Cyclic Peptides Based on Molecular Dynamics Simulations”, 21st IUPAB and 62nd BSJ joint congress 2024 and 26P-210, 2024/6/26.

[C-5] Keisuke Yanagisawa, Yoshitaka Moriwaki, Tohru Terada, Kentaro Shimizu, “Systematic construction of the cosolvents sets for cosolvent MD (CMD) with the large-scale simulation”, AHeDD2019/IPAB2019 Joint Symposium, 2019/11/29.

[C-4] Masahito Ohue, Takanori Hayashi, Yuri Matsuzaki, Keisuke Yanagisawa, Yutaka Akiyama, “Megadock-Web: An Integrated Database of High-Throughput Structure-Based Protein-Protein Interaction Predictions”, Biophysical Society 63rd Annual Meeting and 2792-Pos, 2019/3/02.

[C-3] Keisuke Yanagisawa, Shunta Komine, Shogo D. Suzuki, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “Spresso: An ultrafast compound pre-screening method based on compound fragmentation”, Biophysical Society 62nd Annual Meeting, 2018/2/17.

[C-2] Rikuto Kubota, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama, “Toward efficient protein-ligand docking for virtual screening by reuse of fragments”, The 16th Asia Pacific Bioinformatics Conference (APBC2018) and Poster C5, 2018/1/15.

[C-1] Keisuke Yanagisawa, Shunta Komine, Masahito Ohue, Takashi Ishida, Yutaka Akiyama, “Fast pre-filtering for virtual screening based on compound fragmentation”, 3rd IIT Madras – Tokyo Tech Joint Symposium on Algorithms and Applications of Bioinformatics and P34, 2015/11/05.

国内口頭発表

[D-44] 柳澤 渓甫，藤江 拓哉，高畠 和輝，秋山 泰, “Frasco-VS：フラグメントに基づく薬剤候補化合物選抜の量子アニーリングによる実現”, 情報処理学会研究報告, 2026-QS-17(5): 1–8, 2026/3/16.

[D-43] 清水 正浩，杉田 昌岳，柳澤 渓甫，秋山 泰, “REST/REUS分子動力学のパラメータ自動調整手法の開発”, 情報処理学会研究報告, 2026-BIO-84(26): 1–8, 2026/3/13.

[D-42] 柳澤 渓甫，吉野 龍ノ介，工藤 玄己，広川 貴次, “Inverse MSMD法による化合物部分構造プロファイリングと結合親和性推定”, 情報処理学会研究報告, 2026-BIO-84(19): 1–8, 2026/3/13.

[D-41] 清水 正義，米山 慧，柳澤 渓甫，秋山 泰, “COFFEE-PRESC：有望なフラグメント配置対に基づく化合物検索を用いたプレスクリーニング手法の開発”, 情報処理学会研究報告, 2026-BIO-84(3): 1–8, 2026/3/12.

[D-40] 赤木 果歩，柳澤 渓甫，秋山 泰, “共溶媒分子動力学を用いたドッキング計算のスコアリング改良によるバーチャルスクリーニング精度の向上”, 情報処理学会研究報告, 2026-BIO-84(4): 1–8, 2026/3/12.

[D-39] 赤木 果歩，柳澤 渓甫，秋山 泰, “共溶媒分子動力学シミュレーションで得られるプローブ原子分布を活用したドッキング計算のスコアリングの改良”, 情報処理学会研究報告, 2025-BIO-82(43): 1–8, 2025/6/22.

[D-38] 米山 慧，柳澤 渓甫，秋山 泰, “立体構造類似性に着目したフラグメントベースドバーチャルスクリーニングのための化合物代表フラグメント集合の決定”, 情報処理学会研究報告, 2025-BIO-82(44): 1–8, 2025/6/22.

[D-37] 清水 正浩，杉田 昌岳，柳澤 渓甫，秋山 泰, “REUS MDのレプリカ設定最適化手法の開発と環状ペプチド膜透過性予測への応用”, 情報処理学会研究報告, 2025-BIO-82(45): 1–8, 2025/6/22.

[D-36] 中野 諒也，柳澤 渓甫，秋山 泰, “フラグメントに基づくタンパク質化合物ドッキングへのイジングモデルの適用”, 情報処理学会研究報告, 2025-BIO-82(46): 1–8, 2025/6/22.

[D-35] 齋藤 那哉，清水 正義，柳澤 渓甫，秋山 泰, “フラグメントの類似性を考慮した化合物立体配座検索システムの構築”, 情報処理学会研究報告, 2025-BIO-81(12): 1–8, 2025/3/7.

[D-34] 赤木 果歩，柳澤 渓甫，秋山 泰, “共溶媒分子動力学法による化合物ドッキング向けのバイアス情報の取得”, 情報処理学会研究報告, 2024-BIO-78(37): 1–8, 2024/6/21.

[D-33] 清水 正義，柳澤 渓甫，秋山 泰, “有望なフラグメント空間配置対に基づく化合物プレスクリーニング手法の開発”, 情報処理学会研究報告, 2024-BIO-78(38): 1–8, 2024/6/21.

[D-32] 柳澤 渓甫，吉野 龍ノ介，工藤 玄己，広川 貴次, “Inverse MSMDシミュレーションによるタンパク質－化合物部分構造相互作用定量的評価手法の開発”, 第24回日本蛋白質科学会年会: [WS-14] バイオインフォマティクスと農芸化学の出会うところ, 2024/6/13.

[D-31] 杉田 昌岳，藤江 拓哉，能祖 雄大，柳澤 渓甫，大上 雅史，秋山 泰, “拡張アンサンブル分子動力学シミュレーションに基づいた環状ペプチドの膜透過性予測技術の開発と応用”, 第24回日本蛋白質科学会年会: [WS-3] ペプチド設計の現在と未来, 2024/6/11.

[D-30] 布部 絢子，柳澤 渓甫，秋山 泰, “フラグメントに基づくバーチャルスクリーニングへの利用などを目指したフラグメント集合の選定”, 情報処理学会研究報告, 2024-BIO-77(31): 1–7, 2024/3/8.

[D-29] 李 佳男，柳澤 渓甫，秋山 泰, “CycPeptMP：マルチレベルの分子特徴とデータ拡張による環状ペプチドの膜透過性予測手法の開発”, 情報処理学会研究報告, 2024-BIO-77(15): 1–8, 2024/3/7.

[D-28] 能祖 雄大，杉田 昌岳，藤江 拓哉，柳澤 渓甫，秋山 泰, “分子動力学シミュレーション軌跡データから抽出した位置依存特徴量を活用した環状ペプチドの膜透過性予測”, 情報処理学会研究報告, 2024-BIO-77(16): 1–8, 2024/3/7.

[D-27] 柳澤 渓甫, “薬剤設計のためにはAlphaFoldはまだまだ足りない”, 第12回生命医薬情報学連合大会 (IIBMP2023): [WS-2] バイオインフォマティクスの8の問題, 2023/9/5.

[D-26] 渡辺 銀河，柳澤 渓甫，秋山 泰, “標的RNAの高次構造予測に基づく低活性ASO候補配列の推測”, 情報処理学会研究報告, 2023-BIO-74(36): 1–8, 2023/7/1.

[D-25] 齋藤 那哉，柳澤 渓甫，秋山 泰, “フラグメント対の相対位置から検索可能な化合物立体配座データベースの構築”, 情報処理学会研究報告, 2023-BIO-74(37): 1–8, 2023/7/1.

[D-24] 李 佳男，柳澤 渓甫，杉田 昌岳，藤江 拓哉，大上 雅史，秋山 泰, “CycPeptMPDB：包括的な環状ペプチド膜透過率データベースの開発”, 情報処理学会研究報告, 2023-BIO-74(38): 1–8, 2023/7/1.

[D-23] Keisuke Yanagisawa，Rikuto Kubota，Yasushi Yoshikawa，Masahito Ohue，Yutaka Akiyama, “REstretto: An efficient protein-ligand docking tool based on a fragment reuse strategy”, CBI学会2022年大会, O2-1, 2022/10/25.

[D-22] Masatake Sugita，Takuya Fujie，Keisuke Yanagisawa，Masahito Ohue，Yutaka Akiyama, “Lipid composition is critical for accurate membrane permeability prediction of cyclic peptides by molecular dynamics simulations”, CBI学会2022年大会, O3-2, 2022/10/25.

[D-21] 能祖 雄大，杉田 昌岳，藤江 拓哉，柳澤 渓甫，大上 雅史，秋山 泰, “分子動力学シミュレーション軌跡データからの環状ペプチドの膜透過性と相関が高い特徴量の抽出”, 情報処理学会研究報告, 2022-BIO-70(50): 1–8, 2022/6/29.

[D-20] 柳澤 渓甫，吉野 龍ノ介，工藤 玄己，広川 貴次, “インバース共溶媒分子動力学法による分子プローブ周辺残基環境の可視化”, 第22回日本蛋白質科学会年会, O7-12, 2022/6/7.

[D-19] 稲垣 雅也，柳澤 渓甫，大上 雅史，秋山 泰, “フラグメント化された化合物立体構造データベースの構築”, 情報処理学会研究報告, 2022-BIO-69(15): 1–8, 2022/3/11.

[D-18] 津嶋 佑旗，柳澤 渓甫，大上 雅史，秋山 泰, “新たなデータセットによる長距離フラグメントリンキング手法の再評価”, 情報処理学会研究報告, 2022-BIO-69(16): 1–8, 2022/3/11.

[D-17] 玉野 史結，伊澤 和輝，柳澤 渓甫，大上 雅史，秋山 泰, “Gapmer型ASOにおけるオフターゲット効果のリスク評価手法の提案”, 情報処理学会研究報告, 2022-BIO-69(7): 1–7, 2022/3/10.

[D-16] 山﨑 眞拓，伊澤 和輝，平田 稜，柳澤 渓甫，大上 雅史，秋山 泰, “結合エネルギーを考慮したゲノムワイドな高速短鎖核酸配列検索手法の開発”, 情報処理学会研究報告, 2022-BIO-69(8): 1–8, 2022/3/10.

[D-15] 杉田 昌岳，杉山 聡，藤江 拓哉，吉川 寧，柳澤 渓甫，大上 雅史，秋山 泰, “分子動力学シミュレーションに基づいた環状ペプチドの膜透過率の大規模予測”, 第58回日本生物物理学会年会, 2-03-1712, 2021/11/25.

[D-14] Masatake Sugita，Satoshi Sugiyama，Takuya Fujie，Yasushi Yoshikawa，Keisuke Yanagisawa，Masahito Ohue，Yutaka Akiyama, “Large-scale membrane permeability prediction of cyclic peptides crossing a lipid bilayer based on enhanced sampling molecular dynamics simulations”, CBI学会2021年大会, O2-1, 2021/10/26.

[D-13] 津嶋 佑旗，柳澤 渓甫，大上 雅史，秋山 泰, “タンパク質表面との結合親和性を考慮した長距離フラグメントリンキング手法の開発”, 情報処理学会研究報告, 2021-BIO-67(1): 1–8, 2021/9/30.

[D-12] 井澤 和也，柳澤 渓甫，大上 雅史，秋山 泰, “標的配列との結合・開放エネルギー推定に基づくアンチセンス核酸の阻害活性モデルの研究”, 情報処理学会研究報告, 2020-BIO-65(7): 1–7, 2021/3/11.

[D-11] 柳澤 渓甫, “共溶媒分子動力学シミュレーションにおける創薬向け共溶媒セットの構築”, 第43回日本分子生物学会年会 (MBSJ2020): [2F-11] フォーラム「インシリコ創薬を支える最先端情報科学」, オンライン開催, 2020/12/3.

[D-10] 久保田 陸人，柳澤 渓甫，吉川 寧，大上 雅史，秋山 泰, “共通な部分構造の再利用による高速なタンパク質リガンドドッキング手法の開発”, 情報処理学会研究報告, 2019-BIO-61(3): 1–8, 2020/3/12.

[D-9] Masahito Ohue，Ryota Ii，Keisuke Yanagisawa，Yutaka Akiyama, “Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph”, IPSJ SIG Technical Report, 2019-MPS-124(3): 1–4, 2019/7/29.

[D-8] 伊井 良太，柳澤 渓甫，大上 雅史，秋山 泰, “分子グラフ上の距離を考慮したグラフ畳込みニューラルネットワークによる化合物活性予測”, 情報処理学会研究報告, 2018-BIO-57(11): 1–8, 2019/3/9.

[D-7] 久保田 陸人，柳澤 渓甫，大上 雅史，秋山 泰, “共通な部分構造の再利用アルゴリズムを用いたタンパク質リガンドドッキング手法の開発”, 情報処理学会研究報告, 2018-BIO-54(42): 1–7, 2018/6/15.

[D-6] 柳澤 渓甫，小峰 駿汰，久保田 陸人，大上 雅史，秋山 泰, “フラグメント伸長型化合物ドッキング計算のための重み付きオフラインキャッシュ問題の厳密解アルゴリズム”, 情報処理学会研究報告, 2017-BIO-50(38): 1–8, 2017/6/25.

[D-5] 柳澤 渓甫，大上 雅史，石田 貴士，秋山 泰, “標的タンパク質の立体構造を用いたリガンド候補化合物の上限サイズの推定による化合物フィルタリング”, 情報処理学会研究報告, 2016-BIO-49(6): 1–7, 2017/3/24.

[D-4] 柳澤 渓甫，小峰 駿汰，鈴木 翔吾，大上 雅史，石田 貴士，秋山 泰, “フラグメント分割に基づく超高速化合物プレスクリーニング手法 ESPRESSO”, 情報処理学会研究報告, 2016-BIO-46(18): 1–7, 2016/7/5.

[D-3] 鈴木 翔吾，柳澤 渓甫，大上 雅史，石田 貴士，秋山 泰, “SVMとDeep Learningに基づくヒトc-Yesキナーゼ阻害化合物の予測”, 情報処理学会研究報告, 2015-BIO-42(36): 1–7, 2015/6/24.

[D-2] Keisuke Yanagisawa，Takashi Ishida，Yuichi Sugiyama，Yutaka Akiyama, “Drug clearance pathway prediction based on semi-supervised learning”, IPSJ SIG Technical Report, 2014-BIO-41(11): 1–6, 2015/3/20.

[D-1] 柳澤 渓甫，石田 貴士，秋山 泰, “半教師付き学習を用いた薬物クリアランス経路予測”, 情報処理学会研究報告, 2014-BIO-38(10): 1–6, 2014/6/26.

国内ポスター発表

[E-42] 富田 馨，柳澤 渓甫，秋山 泰, “量子アニーリングを用いたブラックボックス最適化における事前知識導入による学習効率改善の評価”, 第4回 量子アニーリング及び関連技術に関する研究会, P12, 2026/2/18-19.

[E-41] 柳澤 渓甫，藤江 拓哉，高畠 和輝，秋山 泰, “Frasco-VS：フラグメントに基づく薬剤候補化合物選抜の量子アニーラによる実現”, 第4回 量子アニーリング及び関連技術に関する研究会, P14, 2026/2/18-19.

[E-40] 清水 正浩，杉田 昌岳，柳澤 渓甫，秋山 泰, “Development of an automatic parameter adjustment method for REST/REUS MD and its application to predicting the membrane permeability of cyclic peptides”, CBI学会2025年大会, P01-17, 2025/10/27.

[E-39] 中野 諒也，柳澤 渓甫，秋山 泰, “Improvement of fragment-based protein–ligand docking using the Quantum Annealer”, CBI学会2025年大会, P06-19, 2025/10/27.

[E-38] 赤木 果歩，柳澤 渓甫，秋山 泰, “Enhancing virtual screening accuracy by refining docking calculation scoring with mixed-solvent molecular dynamics”, CBI学会2025年大会, P06-16, 2025/10/27.

[E-37] 米山 慧，柳澤 渓甫，秋山 泰, “Construction of representative fragment sets based on mutual 3D structural similarity and docking feasibility for fragment-based virtual screening”, CBI学会2025年大会, P06-15, 2025/10/27.

[E-36] 清水 正義，柳澤 渓甫，秋山 泰, “COFFEE-PRESC: a fast pre-screening method using chemical compound retrieval by fragment pose pairs”, CBI学会2025年大会, P06-13, 2025/10/27.

[E-35] 柳澤 渓甫，吉野 龍ノ介，工藤 玄己，広川 貴次, “Quantitative Estimation of Protein-Ligand Substructure Interaction with Inverse Mixed-Solvent Molecular Dynamics Simulation”, CBI学会2025年大会, P01-26, 2025/10/27.

[E-34] 杉田 昌岳，寺倉 慶，藤江 拓哉，柳澤 渓甫，秋山 泰, “Analysis of membrane permeation processes of cyclic peptides on multiple reaction coordinates based on the Markov state model”, CBI学会2025年大会, P01-06, 2025/10/27.

[E-33] 杉田 昌岳，寺倉 慶，藤江 拓哉，柳澤 渓甫，秋山 泰, “Markov state Model に基づいた環状ペプチド膜透過過程の多次元の反応座標における速度論的な解析”, 第63回日本生物物理学会年会, 2Pos175, 2025/9/24.

[E-32] 赤木 果歩，柳澤 渓甫，秋山 泰, “共溶媒分子動力学法におけるプローブ原子分布を活用したドッキング計算のスコアリングの改良”, 第25回日本蛋白質科学会年会, 1P-061, 2025/6/18.

[E-31] 清水 正浩，杉田 昌岳，柳澤 渓甫，秋山 泰, “REUS MDのレプリカパラメータ最適化手法の開発と環状ペプチド膜透過性予測への応用”, 第25回日本蛋白質科学会年会, 1P-064, 2025/6/18.

[E-30] 寺倉 慶，杉田 昌岳，藤江 拓哉，柳澤 渓甫，秋山 泰, “環状ペプチドの膜透過過程の Markov state Model に基づいた速度論的な解析”, 第25回日本蛋白質科学会年会, 2P-044, 2025/6/18.

[E-29] 本野 千恵，柳澤 渓甫，小関 準，今井 賢一郎, “CrypTothML: 共溶媒分子動力学計算と機械学習を組み合わせたクリプティックサイト予測手法”, 第25回日本蛋白質科学会年会, 3P-038, 2025/6/18.

[E-28] 杉田 昌岳，能祖 雄大，李 佳男，藤江 拓哉，柳澤 渓甫，秋山 泰, “分子動力学シミュレーションと機械学習を組み合わせた環状ペプチド膜透過性の予測法の開発”, 第25回日本蛋白質科学会年会, 3P-040, 2025/6/18.

[E-27] 工藤 玄己，柳澤 渓甫，吉野 龍ノ介，広川 貴次, “共溶媒分子動力学法を用いたタンパク質－タンパク質相互作用プロファイル”, 第52回構造活性相関シンポジウム, KP20, 2024/12/12.

[E-26] Kei Terakura，Masatake Sugita，Keisuke Yanagisawa，Yutaka Akiyama, “Kinetic Analysis of Membrane Permeation Process of Cyclic Peptides Using Markov State Models with Molecular Dynamics Simulations”, CBI学会2024年大会, P01-12, 2024/10/28.

[E-25] Jianan Li，Keisuke Yanagisawa，Yutaka Akiyama, “CycPeptMP: Development of Membrane Permeability Prediction Model of Cyclic Peptides with Multi-Level Molecular Features and Data Augmentation”, CBI学会2024年大会, P03-11, 2024/10/28.

[E-24] Jun Koseki，Chie Motono，Keisuke Yanagisawa，Ryunosuke Yoshino，Takatsugu Hirokawa，Kenichiro Imai, “Development of the Cryptic Site searching method with Mixed-solvent molecular dynamics and Topological data analyses methods”, CBI学会2024年大会, P04-03, 2024/10/28.

[E-23] Masatake Sugita，Yudai Noso，Takuya Fujie，Jianan Li，Keisuke Yanagisawa，Yutaka Akiyama, “Development of Prediction Models for Membrane Permeability of Cyclic Peptides using 3D Descriptors obtained from Molecular Dynamics Simulations and 2D Descriptors”, CBI学会2024年大会, P07-05, 2024/10/28.

[E-22] Keisuke Yanagisawa，Takuya Fujie，Kazuki Takabatake，Yutaka Akiyama, “QUBO Problem Formulation of Fragment-Based Protein–Compound Flexible Docking”, CBI学会2024年大会, P07-14, 2024/10/28.

[E-21] Kaho Akaki，Keisuke Yanagisawa，Yutaka Akiyama, “Acquisition of Bias Information for Protein-Ligand Docking by Mixed-Solvent Molecular Dynamics”, CBI学会2024年大会, P07-15, 2024/10/28. (Like! Poster Award 受賞（学会参加者投票による選出）)

[E-20] Masayoshi Shimizu，Keisuke Yanagisawa，Yutaka Akiyama, “Development of a compound pre-screening method based on docking of fragments”, CBI学会2024年大会, P07-16, 2024/10/28.

[E-19] Tomoya Saito，Keisuke Yanagisawa，Yutaka Akiyama, “Development of an efficient compound 3D conformer search system based on relative position of fragments”, CBI学会2024年大会, P07-33, 2024/10/28.

[E-18] 本野 千恵，柳澤 渓甫，工藤 玄己，広川 貴次，今井 賢一郎, “共溶媒分子動力学シミュレーションによるクリプティックサイト予測”, 第24回日本蛋白質科学会年会, 3P-063, 2024/6/11.

[E-17] Masatake Sugita，Takuya Fujie，Keisuke Yanagisawa，Masahito Ohue，Yutaka Akiyama, “Development of a Protocol for Predicting Membrane Permeability of Cyclic Peptides Based on Molecular Dynamics Simulations”, The 61st Annual Meeting of The Biophysical Society of Japan, 2Pos183, 2023/11/14.

[E-16] Keisuke Yanagisawa，Ryunosuke Yoshino，Genki Kudo，Takatsugu Hirokawa, “Quantitative Evaluation of Protein-Chemical Substructure Interaction with Inverse Mixed-Solvent Molecular Dynamics Simulation”, 第61回日本生物物理学会年会, 2023/11/14.

[E-15] Keisuke Yanagisawa，Ryunosuke Yoshino，Genki Kudo，Takatsugu Hirokawa, “Quantitative Estimation of Protein-Chemical Substructure Interaction with Inverse Mixed-Solvent Molecular Dynamics Simulation”, CBI学会2023年大会, 2023/10/23.

[E-14] Genki Kudo，Keisuke Yanagisawa，Ryunosuke Yoshino，Takatsugu Hirokawa, “Amino Acid Preference Mapping on Protein-Protein Interaction Surface using Mixed-Solvent Molecular Dynamics”, CBI学会2022年大会, P02-04, 2022/10/25.

[E-13] 柳澤 渓甫，吉野 龍ノ介，工藤 玄己，広川 貴次, “インバース共溶媒分子動力学法による分子プローブ周辺アミノ酸残基環境の可視化”, 第60回日本生物物理学会年会, 1Pos031, 2022/9/28.

[E-12] 杉田 昌岳，杉山 聡，藤江 拓哉，吉川 寧，柳澤 渓甫，大上 雅史，秋山 泰, “分子動力学シミュレーションに基づいた環状ペプチドの膜透過率の大規模予測”, 第43回溶液化学シンポジウム, P38, 2021/10/29.

[E-11] Keisuke Yanagisawa，Yoshitaka Moriwaki，Tohru Terada，Kentaro Shimizu, “Systematic construction of the cosolvents sets for cosolvent MD (CMD) with the large-scale computation”, Chem-Bio Informatics Society(CBI) Annual Meeting 2019, P1-24, 2019/10/22.

[E-10] Keisuke Yanagisawa，Yoshitaka Moriwaki，Tohru Terada，Kentaro Shimizu, “Estimation of the probability map (Pmap) similarity of cosolvent MD (CMD) from structural similarities of cosolvents”, The 57th Annual Meeting of The Biophysical Society of Japan, 1Pos012, 2019/9/24.

[E-9] Juanjuan Lu，Keisuke Yanagisawa，Takashi Ishida, “Development of a novel linear notation of chemical compounds for deep learning”, Chem-Bio Informatics Society(CBI) Annual Meeting 2018, P5-17, 2018/10/9.

[E-8] Ryota Ii，Keisuke Yanagisawa，Masahito Ohue，Yutaka Akiyama, “大域的化合物特徴を表現するグラフ畳み込みネットワーク”, Informatics in Biology, Medicine and Pharmacology 2018 (IIBMP2018), P-76, 2018/9/19.

[E-7] Rikuto Kubota，Keisuke Yanagisawa，Masahito Ohue，Yutaka Akiyama, “Development of efficient protein-ligand docking method for virtual screening by reuse of fragments”, 1st RWBC-OIL Workshop, Poster no. 18, 2018/5/8.

[E-6] Masahito Ohue，Takanori Hayashi，Yuri Matsuzaki，Keisuke Yanagisawa，Yutaka Akiyama, “MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions”, Informatics in Biology, Medicine and Pharmacology 2017 (IIBMP2017), P57, 2017/9/27.

[E-5] Masahito Ohue，Takanori Hayashi，Yuri Matsuzaki，Keisuke Yanagisawa，Yutaka Akiyama, “MEGADOCK-WEB: タンパク質間相互作用予測の統合データベース”, 第55回日本生物物理学会年会, 3Pos174, 2017/9/21.

[E-4] Keisuke Yanagisawa，Shunta Komine，Shogo D. Suzuki，Masahito Ohue，Takashi Ishida，Yutaka Akiyama, “ESPRESSO: An ultrafast compound pre-screening method with segmented compounds”, Chem-Bio Informatics Society(CBI) Annual Meeting 2016, P2-19, 2016/10/25.

[E-3] Keisuke Yanagisawa，Shunta Komine，Shogo D. Suzuki，Masahito Ohue，Takashi Ishida，Yutaka Akiyama, “ESPRESSO: An ultrafast compound pre-screening method based on compound segmentation”, Informatics in Biology, Medicine and Pharmacology 2016 (IIBMP2016), P65, 2016/9/29.

[E-2] Keisuke Yanagisawa，Shunta Komine，Masahito Ohue，Takashi Ishida，Yutaka Akiyama, “Fast pre-filtering for virtual screening based on ligand decomposition”, 第21回 創剤フォーラム若手研究会, P-6, 2015/11/28.

[E-1] Keisuke Yanagisawa，Shunta Komine，Masahito Ohue，Takashi Ishida，Yutaka Akiyama, “Fast pre-filtering for virtual screening based on compound decomposition”, Informatics in Biology, Medicine and Pharmacology 2015 (IIBMP2015), 2015/10/29.